For software level evaluation, we adopt the standard evaluation metrics
following [1]}, [2]}, [3]}.
Particularly, we use mean intersection over union (mIoU) for semantic
segmentation, human parts segmentation, and saliency; mean error (mErr) for surface normals estimation, root mean square
error (rmse) for depth estimation; and optimal dataset F-measure (odsF)
[4]} for edge detection. Following [1]}, we use \(\Delta _{m}\)  to evaluate a MTL
model \(m\)  as the average per task drop with respect to the STL model \(b\)  over all tasks: \(\Delta _{m}=\frac{1}{T}\sum _{i}^{T}(-1)^{l_{i}}(M_{m,i}-M_{b,i})/M_{b,i}\) , where \(M_{m,i}\)  and \(M_{b,i}\)  are the metrics of task \(i\)  for
the model \(m\)  and \(b\)  respectively,
and \(l_{i}=1\)  if a lower value means better performance.
